18F-fluorodeoxyglucose positron-emission tomography-computed tomography to diagnose recurrent cancer
Bibliographic record
Abstract
BACKGROUND: Sometimes the diagnosis of recurrent cancer in patients with a previous malignancy can be challenging. This prospective cohort study assessed the clinical utility of (18)F-fluorodeoxyglucose positron-emission tomography-computed tomography ((18)F-FDG PET-CT) in the diagnosis of clinically suspected recurrence of cancer. METHODS: Patients were eligible if cancer recurrence (non-small-cell lung (NSCL), breast, head and neck, ovarian, oesophageal, Hodgkin's or non-Hodgkin's lymphoma) was suspected clinically, and if conventional imaging was non-diagnostic. Clinicians were asked to indicate their management plan before and after (18)F-FDG PET-CT scanning. The primary outcome was change in planned management after (18)F-FDG PET-CT. RESULTS: Between April 2009 and June 2011, 101 patients (age, median 65 years; 55% female) were enroled from four cancer centres in Ontario, Canada. Distribution by primary tumour type was: NSCL (55%), breast (19%), ovarian (10%), oesophageal (6%), lymphoma (6%), and head and neck (4%). Of the 99 subjects who underwent (18)F-FDG PET-CT, planned management changed after (18)F-FDG PET-CT in 52 subjects (53%, 95% confidence interval (CI), 42-63%); a major change in plan from no treatment to treatment was observed in 38 subjects (38%, 95% CI, 29-49%), and was typically associated with (18)F-FDG PET-CT findings that were positive for recurrent cancer (37 subjects). After 3 months, the stated post-(18)F-FDG PET-CT management plan was actually completed in 88 subjects (89%, 95% CI, 81-94%). CONCLUSION: In patients with suspected cancer recurrence and conventional imaging that is non-diagnostic, (18)F-FDG PET-CT often provides new information that leads to important changes in patient management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".